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Updated: Apr 25, 2026

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CLIP Graph Adaptor: A Dual-Graph Adapted Visual-Language Model for Weakly Supervised Semantic Segmentation.

Jia Zhang, Bo Peng, Xi Wu

    IEEE Transactions on Neural Networks and Learning Systems
    |April 23, 2026
    PubMed
    Summary
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    This study introduces CLIP Graph Adapter (CLIP-GA) to improve weakly supervised semantic segmentation by integrating textual and visual knowledge. CLIP-GA generates better initial object class maps, enhancing segmentation accuracy and generalization.

    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Weakly supervised semantic segmentation (WSSS) utilizes contrastive language-image pretraining (CLIP) for pseudo-label generation.
    • Direct CLIP application in WSSS leads to suboptimal transferability and generalization due to ignored interclass relationships.

    Purpose of the Study:

    • To propose CLIP Graph Adapter (CLIP-GA), a novel approach for generating high-quality initial class activation maps (CAMs).
    • To enhance WSSS performance by integrating textual and visual structural knowledge, addressing limitations of direct CLIP model application.

    Main Methods:

    • Introduced a dual-graph adaptive strategy with textual and visual subgraphs.
    • Employed cross-modal graph attention (CGA) for effective knowledge fusion.

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  • Utilized specialized loss functions, superpixel consistency, and graph reasoning attention (GRA) for pseudo-label refinement and global contextual understanding.

    • Main Results:

    • CLIP-GA effectively generates high-quality initial CAMs by integrating structural knowledge.
    • The approach demonstrated superior performance compared to state-of-the-art methods on PASCAL VOC 2012 and MS COCO 2014 datasets.
    • Achieved improved object region completeness and reduced background activation.

    Conclusions:

    • CLIP-GA significantly advances WSSS by effectively leveraging multimodal structural information.
    • The proposed method offers improved transferability and generalization for semantic segmentation tasks.
    • The integration of graph-based strategies enhances the quality of pseudo-labels and final segmentation outcomes.